Spectroscopy is widely used to characterize pharmaceutical products or processes, especially due to its desirable characteristics of being rapid, cheap, non-invasive/non-destructive and applicable ...both off-line and in-/at-/on-line. Spectroscopic techniques produce profiles containing a high amount of information, which can profitably be exploited through the use of multivariate mathematic and statistic (chemometric) techniques. The present paper aims at providing a brief overview of the different chemometric approaches applicable in the context of spectroscopy-based pharmaceutical analysis, discussing both the unsupervised exploration of the collected data and the possibility of building predictive models for both quantitative (calibration) and qualitative (classification) responses.
•The present work provides a novel approach for the authentication of the Avola almond, labelled by the PAT mark.•Avola almonds have been analyzed by NIR spectroscopy coupled with PLS-DA or SIMCA.•· ...PLS-DA led to 100% of correct classification (in external validation) for samples belonging to the category of interest.•Variable Importance in Projection has highlighted which NIR variables contribute the most to the classification.
Avola almond is part of the “Traditional Italian Agri-food Product” (PAT) list, as established by The Italian Ministry of agricultural food, forestry and tourism policies; this endorsement testifies its status as a high added-value product, and, consequently, it highlights the need of analytical methodologies suitable for its authentication. For these reasons, in the present study, the possibility of developing a non-destructive approach, aimed at distinguishing almonds cultivated in the Avola area from others presenting a different geographical origin, has been investigated. To fulfil this purpose, 227 almonds, cultivated in the Avola area or in other Italian territories, have been analysed by near infrared (NIR) spectroscopy coupled with Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modelling of Class Analogies (SIMCA). The two tested approaches achieved satisfactory results (in external validation) indicating both of them would represent a suitable tool for the purpose of the study.
In the last decades, spectroscopic techniques have played an increasingly crucial role in analytical chemistry, due to the numerous advantages they offer. Several of these techniques (e.g., ...Near-InfraRed—NIR—or Fourier Transform InfraRed—FT-IR—spectroscopy) are considered particularly valuable because, by means of suitable equipment, they enable a fast and non-destructive sample characterization. This aspect, together with the possibility of easily developing devices for on- and in-line applications, has recently favored the diffusion of such approaches especially in the context of foodstuff quality control. Nevertheless, the complex nature of the signal yielded by spectroscopy instrumentation (regardless of the spectral range investigated) inevitably calls for the use of multivariate chemometric strategies for its accurate assessment and interpretation. This review aims at providing a comprehensive overview of some of the chemometric tools most commonly exploited for spectroscopy-based foodstuff analysis and authentication. More in detail, three different scenarios will be surveyed here: data exploration, calibration and classification. The main methodologies suited to addressing each one of these different tasks will be outlined and examples illustrating their use will be provided alongside their description.
In the present work, a fast, relatively cheap, and green analytical strategy to identify and quantify the fraudulent (or voluntary) addition of a drug (alprazolam, the API of Xanax®) to an alcoholic ...drink of large consumption, namely gin and tonic, was developed using coupling near-infrared spectroscopy (NIR) and chemometrics. The approach used was both qualitative and quantitative as models were built that would allow for highlighting the presence of alprazolam with high accuracy, and to quantify its concentration with, in many cases, an acceptable error. Classification models built using partial least squares discriminant analysis (PLS-DA) allowed for identifying whether a drink was spiked or not with the drug, with a prediction accuracy in the validation phase often higher than 90%. On the other hand, calibration models established through the use of partial least squares (PLS) regression allowed for quantifying the drug added with errors of the order of 2–5 mg/L.
Fish and other seafood products have a limited shelf life due to favorable conditions for microbial growth and enzymatic alterations. Various preservation and/or processing methods have been ...developed for shelf-life extension and for maintaining the quality of such highly perishable products. Freezing and frozen storage are among the most commonly applied techniques for this purpose. However, frozen-thawed fish or meat are less preferred by consumers; thus, labeling thawed products as fresh is considered a fraudulent practice. To detect this kind of fraud, several techniques and approaches (e.g., enzymatic, histological) have been commonly employed. While these methods have proven successful, they are not without limitations. In recent years, different emerging methods have been investigated to be used in place of other traditional detection methods of thawed products. In this context, spectroscopic techniques have received considerable attention due to their potential as being rapid and non-destructive analytical tools. This review paper aims to summarize studies that investigated the potential of emerging techniques, particularly those based on spectroscopy in combination with chemometric tools, to detect frozen-thawed muscle foods.
Among grains, rice is one of the most widely consumed cereals in the world; it represents a staple food in great part of Asia and Africa, and it is also broadly diffused in America and Europe. One of ...the main issues of storing rice is to protect it from animal attacks; in particular, it is prone to insect infestation. Despite all the attempts made to avoid it (developing new physical barriers, traps and repellants), often food pests manage to break into granary and parcels, contaminating stored commodities. As a consequence, possible infestations must be continuously checked by producers and/or retailers. Different methods have been developed to detect insects in stored commodities, and, despite some of them demonstrated to perform well, they present the substantial limitation of being destructive. This latter characteristic undoubtedly leads to an obvious loss of product (and consequently, of profit), affecting farmers, retailers, and, finally, consumers. For this reason, the aim of the present work is to develop a methodology for the identification of insect infestation in stored rice by NIR spectroscopy coupled with discriminant and modeling classification methods. In particular, among all the different pests possibly present in granaries, the focus has been on detection of the Indian-meal moth (Plodia interpunctella), because it is considered one of the most common infesting insects. Different samples of rice, both infested and edible, coming from different farmers located in six different Countries (Cambodia, India, Italy, Pakistan, Suriname and Thailand) have been analyzed by NIR spectroscopy. Consequently, two different classification methods, Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA) have been applied in order to distinguish among infested and edible samples. In particular, PLS-DA allows correctly classifying 95.6% of the edible 97.5% of the contaminated samples (on the external validation set), whereas the SIMCA model, built only for the category of non-contaminated individuals, resulted highly specific (about 97%) but poorly sensitive on the test specimens. This latter approach (SIMCA) provided better predictions (in particular, in terms of sensitivity) when separate individual models were built subdividing samples in agreement with their country of origin.
•Non-destructive approach for detecting insect infestation in rice•Coupling of NIR spectroscopy and chemometric classification techniques•Very good classification accuracy using partial least squares discriminant analysis•Class modeling (SIMCA) performs better on single-country data sets.
One-hundred and fourteen samples of saffron harvested in four different Italian areas (three in Central Italy and one in the South) were investigated by IR and UV-Vis spectroscopies. Two different ...multi-block strategies, Sequential and Orthogonalized Partial Least Squares Linear Discriminant Analysis (SO-PLS-LDA) and Sequential and Orthogonalized Covariance Selection Linear Discriminant Analysis (SO-CovSel-LDA), were used to simultaneously handle the two data blocks and classify samples according to their geographical origin. Both multi-block approaches provided very satisfying results. Each model was investigated in order to understand which spectral variables contribute the most to the discrimination of samples, i.e., to the characterization of saffron harvested in the four different areas. The most accurate solution was provided by SO-PLS-LDA, which only misclassified three test samples over 31 (in external validation).
The aim of the present work was to develop a green multi-platform methodology for the quantification of l-DOPA in solid-state mixtures by means of MIR and NIR spectroscopy. In order to achieve this ...goal, 33 mixtures of racemic and pure l-DOPA were prepared and analyzed. Once spectra were collected, partial least squares (PLS) was exploited to individually model the two different data blocks. Additionally, three different multi-block approaches (mid-level data fusion, sequential and orthogonalized partial least squares, and sequential and orthogonalized covariance selection) were used in order to simultaneously handle data from the different platforms. The outcome of the chemometric analysis highlighted the quantification of the enantiomeric excess of l-DOPA in enantiomeric mixtures in the solid state, which was possible by coupling NIR and PLS, and, to a lesser extent, by using MIR. The multi-platform approach provided a higher accuracy than the individual block analysis, indicating that the association of MIR and NIR spectral data, especially by means of SO-PLS, represents a valid solution for the quantification of the l-DOPA excess in enantiomeric mixtures.
Headspace Solid-Phase Microextraction coupled to Gas-Chromatography with Mass Spectrometry detection (HS-SPME/GC-MS) has been widely used to analyze the composition of wine aroma. This technique was ...here applied to investigate the volatile profile of Trebbiano d'Abruzzo and Pecorino white wines produced in Abruzzo (Italy). Optimization of SPME conditions was conducted by Design of Experiments combined with Response Surface Methodology. We investigated the influence of the kind of sorbent, PDMS, CW/DVB, or PDMS/CAR/DVB, and the effect of the fiber exposure time, temperature, and salt concentration on the total area of the chromatogram and the extraction efficiency of ethyl decanoate and 3-methyl-1-butanol, representative of apolar and polar compounds, respectively. The PDMS/CAR/DVB sorbent allowed the extraction of about 70 compounds, whereas only a part of these substances could be extracted on the PDMS and CW/DVB fibers. Reliable response surfaces for the total area and peak areas of the selected volatiles collected on the PDMS and PDMS/CAR/DVB sorbents and, in the latter case, principal component analysis were evaluated to find the optimal conditions. The optimized extraction conditions were applied for a preliminary comparison of the volatile profile of the two wine varieties and in a successive varietal discrimination study based on data-fusion approaches.
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